WEBSITE APPENDIX A Personality, Abilities and Motivations

WEBSITE APPENDIX A
Personality, Abilities and Motivations
Table 1. Individual differences widely studied in psychology
Definition (APA Dictionary definition
Individual Difference
in quotes)
Personality Traits: How individuals typically act, think, and feel
Example Measures
Big Five personality model
Big Five Openness to
Experience
Big Five Conscientiousness
Big Five
Neuroticism/Emotional
Stability
Big Five Agreeableness
Big Five Extraversion
“A model of the primary dimensions of
individual differences in personality. The
dimensions are usually labeled extraversion,
neuroticism, agreeableness, conscientiousness,
and openness to experience, thought he labels
vary somewhat among researchers.”
“A dimension of the Big Five personality
model that refers to individual differences in
the tendency to be open to new aesthetic,
cultural, or intellectual experiences.”
Openness correlates with IQ (r about .3). Also
called Intellect, this dimension includes facets
such as open-mindedness, creativity,
appreciation of arts and music.
“The tendency to be organized, responsible,
and hardworking, construed as one end of a
dimension of individual differences
(conscientiousness vs. lack of direction) in the
Big Five personality model.”
Conscientiousness predicts work and health
outcomes and includes facets such as
dependability, orderliness, perseverance, and
need for achievement.
“One of the dimensions of the…Big Five
personality model characterized by a chronic
level of emotional instability and proneness to
psychological distress.” This dimension is
often termed emotional stability, which is the
opposite of neuroticism, and includes facets
such as hostility, depression, and anxiety.
“The tendency to act in a cooperative,
unselfish manner, construed as one end of a
dimension of individual differences
(agreeableness vs. disagreeableness) in the
Big Five personality model.” This dimension
includes facets such as trust and compliance.
“An orientation of one’s interests and energies
toward the outer world of people and things
rather than the inner world of subjective
experience. Extraversion is a broad
personality trait and, like introversion, exists
NEO-PI-R (Costa &
McCrae, 1992); Big Five
Inventory (John &
Srivastava)
Typical Intellectual
Engagement (TIE,
Ackerman); Need for
Cognition (Cacioppo);
Openness domain of any
Big Five questionnaire
Conscientiousness domain
of any Big Five
questionnaire
Neuroticism domain of any
Big Five questionnaire.
Negative affect measures
could also be used but the
latter often emphasize
temporary affect rather than
dispositional affect.
Agreeableness domain of
any Big Five questionnaire.
Extraversion domain of any
Big Five questionnaire.

Social vitality
Social dominance
Need for
Achievement/Ambition
Delay of
gratification/Impulsivity/Self-
Control/Time Preference
Sensation Seeking
Perceived Self-efficacy/locus of
control/optimism
on a continuum of attitudes and behaviors.
Extroverts are relatively more outgoing,
gregarious, sociable, and openly expressive.”
Roberts (2006) suggests that there are two
aspects of Extraversion: social dominance and
social vitality. See below.
A subdimension of Big Five Extraversion
proposed by Roberts (2006) that includes
facets such as sociability, positive effect, and
gregariousness.
A subdimension of Big Five Extraversion that
includes facets such as dominance,
independent, and self-confidence, especially
in social settings.
“A strong desire to accomplish goals and
attain a high standard of performance and
personal fulfillment. People with high need for
achievement undertake tasks in which there is
a reasonable probability of success and avoid
tasks that are too easy or too difficult”
A facet of Big Five Conscientiousness. Desire
to achieve difficult goals or reach high
standards.
“forgoing immediate reward in order to obtain
a larger or more desirable reward in the
future”
A facet of either Neuroticism or
Conscientiousness. When faced with a choice
between immediate temptation and superior,
deferred gratification, the ability to control
behavior, attention, and thoughts in order to
attain superior, deferred reward.
“The tendency to search out and engage in
thrilling activities as a method of increasing
stimulation and arousal. It takes the form of
engaging in highly stimulating activities
accompanied by a perception of danger.” A
facet of either Conscientiousness or
Extraversion. Attraction to novel, intense
experiences and willingness to take risks for
them.
Perceived Self Efficacy: “Subjective
perception of own capability of performance
or ability to attain results”. Locus of Control:
“perception of how much control individuals
have over conditions of their lives”. Possibly a
facet of Neuroticism. The belief that one has
control over outcomes. The opposite of
helplessness.
CPI Sociability Scale; NEO-
PI-R Gregariousness and
Activity Scales
NEO-PI-R Assertiveness
scale; 16PF Dominance
Scale
Projective measures
(unreliable),
Conscientiousness subscales
Marshmallow task
(Mischel); UPPS Impulsive
Behavior Subscale
(Whiteside & Lynam); Self-
Control Scale (Baumeister);
Time Perspective Inventory
(Zimbardo)
Sensation-seeking Scale
Generalized self-efficacy
scales 41 , Rotter Locus of
Control Scale, Attributional
Style Questionnaire
41 Bandura does not endorse any trait-level scales
1

Regulatory Focus
Affect, Well-Being
Self-esteem
Temperament (childhood)
Psychopathology
Orientation toward either promotion of
positive outcomes or prevention of negative
outcomes.
Positive affect is the internal feeling state that
occurs when a goal has been attained, a source
if threat has been avoided or the individual is
satisfied with the present state of affairs”
Positive affect includes emotions such as joy,
contentment, and pride. Negative effect,
which is only moderately inversely correlated
with positive effect, includes fear, anxiety,
sadness, etc. Life satisfaction is a cognitive,
not affective, appraisal of the quality of one’s
life. Well-being is characterized by the
presence of positive effect, the absence of
negative effect, and positive life satisfaction.
“The degree to which the qualities and
characteristics contained in one’s self concept
are perceived to be positive”
One's estimation of one's own self-worth. A
construct that enjoyed tremendous popularity
in the 1970ss but since has been considered
epiphenomenal not causal. A minority of
psychologists consider positive and negative
evaluations of the self to be the sixth and
seventh factors of personality. Possibly
grouped with self-efficacy, etc.
“Basic foundation of personality, usually
assumed to be biologically determined and
present early in life[…] Includes
characteristics such as energy level, emotional
responsiveness, response tempo and
willingness to explore”
Precursors to personality traits, temperament
variables are patterns in behavior or affect that
appear early in life that are assumed to have a
neurobiological basis. There are fewer
temperament variables than personality traits
because these individual differences are less
salient and stable in children than in adults.
“Patterns of behavior or thought processes
that are abnormal or maladaptive”. A broad
category comprising dysfunctional patterns of
thought, feeling, or behavior. Most disorders
are included in the DSM-IV manual. Axis I
disorders (e.g., depression) are more intense
and episodic/discreet, whereas Axis II
disorders (i.e., personality disorders) are more
tonic and enduring.
Regulatory Focus
Questionnaire (Higgins et
al., 2001)
PANAS (Watson, Clark, &
Tellegen) for positive and
negative affect; SWLS
(Diener) for life satisfaction
Rosenberg Self-Esteem
Scale
Children's Behavior
Questionnaire
Beck Depression Inventory;
Beck Anxiety Inventory;
MMPI (omnibus measure);
Child Behavior Checklist
(Achenbach)
2

Myers-Briggs Type Indicator
(MPTI)
Type A/Type B personality
Intelligence, General Mental
Ability, “g”, IQ
Specific mental abilities
Creativity
“A personality test designed to classify
individuals according to their expressed
choices between contrasting alternatives in
certain categories of traits. The categories,
based on Jungian typology, are extraversion-
Introversion, Sensing-Intuition, Thinking-
Feeling, and Judging-Perceiving…The test has
little credibility among research psychologists
but is widely used in educational counseling
and human resource management…”
Type A personality is “a personality pattern
characterized by chronic competitiveness,
high levels of achievement motivation, and
hostility.” Type B personality is “a personality
pattern characterized by low levels of
competitiveness and frustration and a relaxed,
easy going approach.”
Intelligence and general mental ability both
refer to “the ability to derive information,
learn from experience, adapt to the
environment, understand and correctly utilize
thought and reason.” “g” (“general factor”)
refers to the first factor extracted from a factor
analysis of cognitive tasks, which many
researchers consider to represent general (vs.
specific) intelligence.”It represents
individuals’ abilities to perceive relationships
and to derive conclusions from them. It is the
basic ability that underlies the performance of
different intellectual tasks”
“ Intelligence quotient (IQ) refers to one’s
intelligence relative to one’s age group
(especially for children) but can also be used
synonymously with intelligence.
“Abilities as measured by tests of an
individual in areas of spatial visualization,
perceptual need, number facility, verbal
comprehension, word fluency, memory,
inductive reasoning and so forth”
An umbrella category for lower-level mental
abilities, including math, verbal, and spatial
abilities, as well as even more specific mental
capacities. 42
“Ability to produce original work, theories,
techniques or thoughts […] Related with
imagination, expressiveness, originality.”
Ability to generate novel ideas and behaviors
that solve problems.
MBTI
WISC, WAIS, Raven’s
Progressive Matrices,
ASVAB
Subtest scores on IQ tests
Creative Personality Scale
(Gough, 1979)
42 Cattell considered fluid (capacity to learn) and crystallized intelligence (knowledge) to be second-order aspects of
intelligence.
3

Executive Function
Cognitive reflection
Emotional Intelligence
“Higher level cognitive processes that
organize and order behavior, including logic
and reasoning, abstract thinking, problem
solving, planning and carrying out and
terminating goal directed behavior” Broad set
of higher-level cognitive capacities attributed
to the prefrontal cortex, often involving the
coordination and management of lower-level
processes.
A specific mental ability. The tendency to
reflect before taking an intuitive answer as
correct.
“Ability to process emotional information and
use it in reasoning and other cognitive
activities. According to Mayer and Slovey
1997 model it comprises four abilities: to
perceive and appraise emotions accurately, to
access and evoke emotions when they
facilitate cognition, to comprehend emotional
language and make use of emotional
information, and to regulate one’s own and
others’ emotions to promote growth and wellbeing”
Ability to perceive emotion, to
integrate it in thought, to understand it and to
manage it (Mayer)
Innumerable
neuropsychology tasks (e.g.,
go/no-go, Stroop,
Continuous Performance
Task)
Cognitive Reflection Test
(Frederick, 2005)
MSCEIT (Mayer &
Salovey) but really there are
no good measures
Motivation – What individuals want to do, feel, or think
“A moral, social or aesthetic principle
accepted by an individual (or society) as a
guide to what is good, desirable or important.”
What individuals feel is important in a moral
Values
sense.
“Attitude characterized by a need to give
selective attention to something that is
significant to the individual”.
What spontaneously attracts and holds one’s
Interests
attention, and is considered pleasant.
Goal: “The end state toward which a human is
striving” Motive:”physiological or
psychological state of arousal that directs an
organism’s energies toward a goal”.
What individuals aim to achieve or
experience. Examples include need for
power, need for affiliation, and need for
achievement. Note that many consider need
Goals, Needs, Motives for achievement as an aspect of personality.
Values in Action Inventory
of Strengths
Self-Directed Search, Strong
Interest Inventory
Thematic Apperception Test
(TAT), need for
achievement questionnaires
4

Figure 1
Problem similar to the Raven’s Progressive Matrices test items
Note: The bottom right entry of this 3x3 matrix of figures is missing and must be selected
from among 8 alternatives. Looking across the rows and down the columns, the test taker
attempts to determine the underlying pattern and then pick the appropriate missing piece.
The correct answer to this problem is 5. Figure taken from Carpenter, Just, and Shell
(1990), used with permission of the publisher, copyright American Psychological
Association.

Measurement Error Can Create the Illusion of Multiple Factors when Only One is
Operative
If some outcome Y
j
is predicted by f k
, and there are multiple mismeasured
proxies for f
k
, those proxies will be predictive for Y
j
even though only one factor
generates the outcome. Thus, if the following relationship holds between Y j and f k , where
U
j
is statistically independent of f
k
,
Y f U ,
j k j
and we use N Q-adjusted proxies for f
k
n
n n n n
M M ( Q)
f
k k k k k k
n
where
k
is independent of f k
, has mean zero and variance
n
means of the measures to depend on Q ( ( Q))
, we obtain an equation
k
N
n
Y
j
nM k U
j ,
n1
2
n
k
, where we allow the
where U j
fk U
j
and where the true values of
n
are all zero, because the test scores
do not determine Y
j
but instead f
k
determines it unless 0 . Straightforward
calculations show that under general conditions, if one arrays the
n
in a vector of length
N, γ N , and the Q-adjusted test scores in a vector of length N, denoted M , the OLS
estimator
N
1
Cov M, M Cov M , Y n
converges in large samples to

2
2 1 2 1 2 2 1 N 2
1 k
f
k kk f
k kk
f
k
k
1
1 2 2 2 2
2
2
k
N
2
2
kk
f
k
k
fk
plim
k
f
.
k
k
N
1 L 2 2 N
2
2
kk
f
N
k
k
f
k
k
1
Assuming
2
0,
0
and 0 , for all n1, , N , any error-ridden predictor of
2
f k
n
k
f
k
will be statistically significant in a large enough sample. Using a purely predictive
criterion to determine personality traits produces a proliferation of “significant”
predictors for outcome Y
j
, as is found in the psychological studies that we survey in the
text. Cunha and Heckman (this issue) show that estimated measurement errors in both
cognitive and noncognitive tests are important so that the problem of proxy proliferation
using a predictive criterion is serious. Under such a criterion, many “significant”
predictors of an outcome can be found that are all proxies for a single latent construct.
If, in addition to these considerations, the measures fail discriminant validity, the
predictors for one outcome may proxy both f
k
and f k
k
k
if f
k
and f k
are
correlated. We present evidence in the text, Section III, that IQ tests proxy both cognitive
and personality factors. A purely predictive criterion fails to distinguish predictors for
clusters associated with the f
k
from predictors for clusters proxying the fk
. Thus, items
in one cluster can be predictive of outcomes more properly allocated to another cluster.
Accounting for Reverse Causality
A test score may predict an outcome because the outcome affects the test score.
Hansen, Heckman, and Mullen (2004) analyze a model in which, in the previous
representation, the outcome being related to cluster (factor) k, say Y
j
, is an element of Q

n
n
and determines
k
( Q)
and
k
( Q)
. In addition, they allow (factor) f
k
to be a
determinant of Y
j
. They establish conditions under which it is possible to identify causal
effects of f
k
on Y
j
when the proxies for f
k
suffer from the problem of reverse causality
n
n
because the Q in ( Q)
and ( Q)
may include Y
j
among its components. They
k
k
establish that tests of cognitive ability (e.g., AFQT) are substantially affected by
schooling levels at the date the test is taken.
To understand how their method works at an intuitive level, consider the effect of
schooling on measured test scores. Schooling attainment likely depends on true or
“latent” ability, f
k
. At the same time, the measured test score depends on schooling
n
n
attained so it affects ( Q)
or ( Q)
or both. Hansen, Heckman, and Mullen (2004)
k
k
assume access to longitudinal data that randomly sample the population. Included in the
sample are adolescents. At the time the adolescents are given the test, persons who
eventually attain the same schooling level are at different grade levels at the date of the
test because the longitudinal sample includes people of different ages and schooling
levels. From the longitudinal data we can determine final schooling levels. Final
schooling levels are assumed to depend on latent ability f k
. Conditional on the final
schooling level attained, schooling levels at the date of the test are random with respect to
f
k
because the sampling rule is random across ages at a point in time. One can identify
the causal effect of schooling on test scores from the effect of variation in the years of
education attained at the date of test on test scores for persons who attain the same final
schooling level. See Hansen, Heckman, and Mullen (2004) for additional details on this
method and alternative identification strategies. The basic idea of their procedure is to

model the dependence between Q and f
k
and to solve the problem of reverse causality
using this model. Heckman, Stixrud and Urzua (2006) develop this method further.
Cunha and Heckman (2007) and Cunha, Heckman and Schennach (2006) extend this
procedure to allow f
k
to evolve over time through investment and experience.

a) Personality
a1. “BIG FIVE”:
The literature on personality psychology widely adopts “the big five” as a taxonomy for describing one’s
personality traits.
The idea is that there are five big dimensions over which personality can be studied. These dimensions are
openness to experience, conscientiousness, neuroticism, agreeableness and extraversion.
Each dimension is then subdivided in lower level traits called “facets”. While there is a relatively broad
consensus over the big five, there is still a wide disagreement over which facets correspond to each
dimension. In the present scheme, we propose one of the possible lower order structure.
Measures for the big five:
- Big Five Inventory, 44 items questionnaire at page 70 of John O., Srivastava S., (1999): “The Big Five
Traits Taxonomy: History, Measurement, and Theoretical Perspectives”:
http://www.uoregon.edu/~sanjay/pubs/bigfive.pdf
- NEO-PI-R (Costa, McCrae, 1992), 240 items. It measures not only the Big Five, but also six "facets" of
each of the Big Five. Commercial product not publicly available. See
http://www3.parinc.com/products/product.aspx?Productid=NEO-PI-R
- NEO FFI, 60 items only measuring the big five. Also not publicly available.
http://www3.parinc.com/products/product.aspx?Productid=NEO-SS_PIR-FFI
For a general introductory literature on the “big five”:
- Costa P., McCrae R. (1999), “A five factors model of personality”, Handbook of Personality, Theory
and research, Guilford Press, New York.
- Digman J., (1990) “Personality Structure: Emergency of the Five factor model”, Annual Review of
Psychology 41, 417-440
- Goldberg L.R., (1990), “An alternative description of personality: the big five factor structure”, Journal
of Personality, 59(6), 1216-1229
- John O., Srivastava S., (1999), “The big five trait taxonomy: history, measurement and Theoretical
Perspectives”, 102-139 in “Handbook of personality: Theory and research”, Guilford Press, New York.
- John O., (1989) “Towards a taxonomy of Personality Descriptors”, in Buss and Cantor Eds, Personality
Psychology: Recent trends and Emerging Directions”, 261-271, Springer, NY.
- John O., Goldber L.R., (1991)” Is there a level of personality description?”, Journal of Personality and
Social Psychology 60, 348-361
- Srivastava, S. (2006). Measuring the Big Five Personality Factors. Retrieved from
http://www.uoregon.edu/~sanjay/bigfive.html
For a discussion of facets:
- Costa, McCrae,Dye,(1992). “Facet scales for agreeableness and conscientiousness: A revision of the
NEO Personality Inventory”. Personality and Individual Differences, 12, 887-898.
- Costa, McCrae (1995): “Domains and facets: Hierarchical Personality Assessment using the revised
NEO PI”, Journal of Personality Assessment 64, 21-50
For further readings on the links between big five and smoking, crime, scholastic achievement and
labor market outcomes.:
1) on smoking and alcohol use:
- T. Trull , C. Waudby, K. Sher K. (2004): “ Alcohol, Tobacco and Drug use disorders and personality
disorder symptoms” Experimental and Clinical Psychopharmacology 12 (1), 65-75; for a review.
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- Zuckermann M., (1993) “Behavioral expression and biosocial bases of sensation seeking” New York,
Cambridge University Press
2) on crime
- Gottfredson and Hirschi 1990, “ A General Theory of Crime”, Stanford, CA: Stanford University Press;
on relationship between self control and crime
- Schaeffer C., Petras H., Ialongo N., Poduscka J., Kellman S., (2003), “Modeling Growth in Boys’
Aggressive Behavior Across Elementary School: Links to Later Criminal Involvement, Conduct
Disorder and Antisocial Personality Disorder” Developmental Psychology 39 (6): 1020-1035; on
relationship between aggression and crime
3) on scholastic achievement
- Borghans L., Meijers H. and Ter Weel B., (2006), “The role of non cognitive skills in explaining non
cognitive test scores” Maastricht University working paper, Maastricht, the Netherlands.
- Duckworth A., Seligman M.(2005),” Self Discipline outdoes IQ in predicting Academic Performance in
Adolescents” Psychological Science 16, 934-944; on self control and scholastic achievement
- Noftle and Robins, in press. I can’t find it!
- Wolfe R. and Johnson S., (1995), “Personality as a Prediction of College Performance”, Educational and
Psychological Measurement, 55 (2), 177-185
4) on labor market performance
- Barrick, Mount, (1991) “ The big five personality dimensions and job performance”, Personnel
Psychology, Blackwell Synergy.
- Roberts B., Wood D., Smith J.(2005) “Evaluating Five Factor Theory and Social Investment
Perspectives on Personality Trait Development”, Journal of Research on Personality 39: 166-184
- Barrick, Higgins, Murray, Chad, Judge, Thoresen (1999) “The Big Five Personality Traits, General
Mental Ability and Career Success across the Life Span”, Personnel Psychology, 52
Roberts, B. W., & Robins, R. W. (2000). Broad dispositions, broad aspirations: The intersection of the
Big Five dimensions and major life goals. Personality and Social Psychology Bulletin, 26, 1284-1296.
- Roberts, B. W., & Robins, R. W. (2004). A longitudinal study of person-environment fit and
personality development”. Journal of Personality, 72, 89-110.
DOMAINS OF THE BIG FIVE:
1. Openness to experience
“A dimension of the Big Five personality model that refers to individual differences in the tendency to
be open to new aesthetic, cultural, or intellectual experiences.” (APA Dictionary)
It is the Big Five domain that correlates most with IQ (r about .3). It is also called Intellect. Includes
facets such as open-mindedness, creativity, appreciation of arts and music.
For its correlation with openness to experience, authors include into this category also J. Cacioppo’s
need for cognition concept: an individual's tendency to engage in and enjoy effortful cognitive
endeavours. For a measure of need for cognition see the 18 items questionnaire at Cacioppo, Kao,
Petty (1984), - http://www.leaonline.com/doi/pdfplus/10.1207/s15327752jpa4803_13.
Another measure related to openness to experience is the TIE, Typical Intellectual Engagement (see
Ackerloff and Goff, 1992, 1994)
For readings on Openness to Experience:
- Cacioppo, Kao, Petty, (1984), “An efficient assessment for need for cognition”, Journal of Personality
Assessment, 48 (3) http://www.leaonline.com/doi/pdfplus/10.1207/s15327752jpa4803_13
- Elias, Loomis (2002) : Utilizing Need for Cognition and Perceived Self-Efficacy to Predict Academic
Performance, Journal of Applied Social Psychology 32 (8), 1687–1702.
- Goff, Ackerman, (1992), “Typical Intellectual Engagement (TIE) measure”, Journal of Educational
Psychology 84(4), December 1992, 537–552
2. Conscientiousness
“The tendency to be organized, responsible, and hardworking, construed as one end of a dimension of
individual differences (conscientiousness vs. lack of direction) in the Big Five personality model.”
(APA Dictionary)
Conscientiousness is the Big Five domain that best predicts work and health outcomes.
6

Most of the work on conscientiousness is done by Brent Roberts. His website link below is a good
source for further readings.
Two well studied facets empirically related to conscientiousness are need for achievement and delay
for gratification.
- Need for achievement is defined as “A strong desire to accomplish goals and attain a high standard of
performance and personal fulfillment. People with high need for achievement undertake tasks in which
there is a reasonable probability of success and avoid tasks that are too easy or too difficult”
- while Delay for gratification is the ability of foregoing immediate reward in order to obtain a larger or
more desirable reward in the future” (APA Dictionary)
The “Marshmallow test” (Mischel 1989) was the first study of the relationship between delayed
gratifications and outcomes later in life. See literature below. There is no single measure for delay of
gratification.
As for need for achievement, it is usually measured by McClelland N-Ach Tematic Apperception Test
(TAT), a projective test that however has come under serious critique from mainstream psychology.
For readings on Conscientiousness
- Brent Roberts papers on conscientiousness are available at his
website,http://www.psych.uiuc.edu/~broberts/Brent%20W%20Roberts%20Research%20Interests.htm
- Need for Achievement:
- McClelland, D.C. (1961) The achieving society. Princeton: Van Nostrand.
- McClelland, D. C., Atkinson, J. W., Clark, R. A., & Lowell, E. L. (1958). A scoring manual for the
achievement motive; in J. W. Atkinson (Ed.), Motives in Fantasy, Action and Society. New York: Van
Nostrand.
- Costa and McCrae, (1992) “ NEO PI-R professional manual”. Odessa, FL: Psychological Assessment
Resources, Inc.
- Delay of Gratification:
- Mischel, Shoda, Rodriguez,. (1989). “Delay of gratification in children”. Science, 244, 933-938.
- Funder, Block (1989) “The role of Ego-Control, Ego-Resiliency and IQ Delay of Gratification in
Adolescence”. Journal of Personality and Social Psychology, 57(6), 1041–1050. A delay gratification
experiment with 14 years old adolescent, evidence that delay behaviour is related with IQ.
- Self Control:
- Roy Baumesteir at http://www.psy.fsu.edu/~baumeistertice/
3. Neuroticism
Neuroticism is “characterized by a chronic level of emotional instability and proneness to psychological
distress.” (APA Dictionary)
It describes the tendency to feel negative emotions, such as anxiety, fear, sadness, and hostility,
particularly when under stress.
Facets empirically loading on neuroticism are self efficacy and locus of control. Although some
literature considers these measures as independent traits of personality, they are much correlated with
each other (with a correlation of about r=.6), as they both refer to the belief to have outcomes under
control. Perceived Self Efficacy (Bandura, A.) is the “Subjective perception of own capability of
performance or ability to attain results”. Self Efficacy is case dependent (one can have highs elf efficacy
in one field but low in another). It is emphasized that self efficacy is different from ability. For example,
some studies have documented that, given ability, girls have lower measure of self efficacy than boys
(Pajares, 1996, among others). Locus of Control is the “perception of how much control individuals
have over conditions of their lives”.
Locus of Control can be internal or external. If internal, the individual attributes events to his own
control: success or insuccess is a consequence of his or her actions. If external, the individual believes
instead that outcomes are a consequence of good or bad luck.
Measures for perceived self efficacy and locus of control:
Perceived Self efficacy:
Bandura does not endorse and trait specific scales, but generalized self efficacy scales can be found into
the “Self Efficacy Measures” paragraph of the website on self efficacy:
http://www.des.emory.edu/mfp/self-efficacy.html
Locus of Control:
7

Rotter has a 29 items questionnaire to measure locus of control, available at
http://wilderdom.com/psychology/loc/RotterLOC29.html
For a measure of optimism: Seligman M., Abramson L., Semmel A., von Baeyer C., Peterson C.:
“Attributional Style Questionnaire: description and assessment”, Cognitive Therapy 6(3), 287-299.
http://www.springerlink.com.proxy.uchicago.edu/content/t372377276jp6636/?p=aded4ffddd064fe8be8
3883d5bb91b78&pi=4
For reading on the facets of self efficacy and locus of control:
- Bandura A., 1977, “Self-efficacy: Toward a unifying theory of behavioral change”
Psychological Review, 84 (2), 191-215
- All of Bandura’ publications can be found in his website: http://www.des.emory.edu/mfp/banpubs.html
- Website on self efficacy: http://www.des.emory.edu/mfp/self-efficacy.html
- Rotter, J.B. (1966). "Generalized expectancies of internal versus external control of reinforcements".
- Judge, Bono, Thoresen (2002), “Are Measures of Self-Esteem, Neuroticism, Locus of Control, and
Generalized Self-Efficacy Indicators of a Common Core Construct?” Journal of Personality and Social
Psychology, 83(3)
4. Agreeableness
“The tendency to act in a cooperative, unselfish manner, construed as one end of a dimension of
individual differences (agreeableness vs. disagreeableness) in the Big Five personality model.” This
dimension includes facets such as trust and compliance. (APA Dictionary)
5. Extraversion
“An orientation of one’s interests and energies toward the outer world of people and things rather than
the inner world of subjective experience. Extraversion is a broad personality trait and, like introversion,
exists on a continuum of attitudes and behaviors. Extroverts are relatively more outgoing, gregarious,
sociable, and openly expressive.” (APA dictionary)
Roberts (2006) suggests that there are two aspects of Extraversion: Social Dominance and Social
Vitality.
- Social Dominance: describes dominance, independent, and self-confidence, especially in social
settings. See NEO PI for a measure.
- Social Vitality: describes sociability, positive effect, and gregariousness. See NEO PI and CPI
(California Psychological Inventory) sociability scale for a measure.
A third facet is empirically related to both extraversion and conscientiousness:
- Sensation Seeking: “the tendency to search out and engage in thrilling activities as a method of
increasing stimulation and arousal. It takes the form of engaging in highly stimulating activities
accompanied by a perception of danger” (APA Dictionary). Zuckermann is the main author for
sensation seeking: he is also the creator of a sensation seeking scale:
- Zuckerman (1979) “Sensation seeking: beyond the optimal level of arousal”. Hillsdale, NJ:Erlbaum. The
scale is online at
http://www.rta.nsw.gov.au/licensing/tests/driverqualificationtest/sensationseekingscale/
Zuckermann has recently published a book on sensation seeking and risky behaviour:
- Zuckermann M. (2006) “Sensation Seeking and Risky Behavior”, Washington, DC : American
Psychological Association.
a2. POSITIVE AFFECT, WELL BEING AND HAPPINESS
“Positive effect is the internal feeling state that occurs when a goal has been attained, a source if threat
has been avoided or the individual is satisfied with the present state of affairs”
Positive affect includes emotions like joy, contentment, and pride. Negative affect, which is only
moderately inversely correlated with positive emotions, includes fear, anxiety, sadness, etc. Life
satisfaction (Diener, Seligman) is a cognitive, not affective, appraisal of the quality of one’s life. Wellbeing
is the presence of positive effect, the absence of negative and positive life satisfaction.
Measures:
8

- SWLS, “Satisfaction with life scale”, (Ed Diener). A 5 questions cognitive, not affective, appraisal of
the quality of one’s life: http://www.psych.uiuc.edu/~ediener/hottopic/hottopic.html
- PANAS, “Positive and Negative Affect Schedule” (Watson, Clark, & Tellegen) for positive and
negative effect. For presentation and for example of PANAS see:
http://www.psychology.uiowa.edu/Faculty/Watson/PANAS-X.pdf
Literature:
- Websites, with links to publications:
http://www.authentichappiness.sas.upenn.edu/
http://www.centreforconfidence.co.uk/pp/contributors.php
http://www.enpp.org/
- Snyder, C. R., Lopez, S. J. (Eds.). Handbook of positive psychology. New York: Oxford University
Press.
- Feldman Barrett L., Russell J., (1999), “The Structure of Current Affect: Controversies and Emerging
Consensus”, Current Directions in Psychological Science 8 (1), 10–14.
- Feldman Barrett L., Mesquita B., Ochsner K., Gross J., (2007) “The Experience of Emotion” Annual
Review of Psychology 58:1, 373
a3. SELF ESTEEM
“The degree to which the qualities and characteristics contained in one’s self concept are perceived to
be positive”
Self-esteem is a positive or negative orientation toward oneself: an overall evaluation of one's worth or
value
It is a construct that enjoyed tremendous popularity in the 1970ss but since has been considered
epiphenomenal not causal. A minority of psychologists consider positive and negative evaluations of
the self to be the sixth and seventh factors of personality.
It is measured with Self Esteem Rosemberg Scale. Link at:
http://www.atkinson.yorku.ca/~psyctest/rosenbrg.pdf
Literature:
- Baumeister, Campbell, Krueger, Vohs, (2003). “Does High Self-esteem Cause Better Performance,
Interpersonal Success, Happiness, or Healthier Lifestyles?” Psychological Science in the Public
Interest, 4, 1-44. http://www.psy.fsu.edu/~baumeistertice/baumeisteretal2003.pdf
- Robins R., Trzesniewski K., Moffit T., Caspi A., Donnellan B., Poulton R., (2006): “Low Self Esteem
During Adolescence predicts poor health, criminal behaviour and limited economic prospects during
adulthood” Developmental Psychology 42(2); in a different perspective than Bauemeister, Robins and
Trzesniewski work show evidence that self esteem does matter.
a4. TEMPERAMENT (childhood)
“Basic foundation of personality, usually assumed to be biologically determined and present early in
life. It Includes characteristics such as energy level, emotional responsiveness, response tempo and
willingness to explore” (APA Dictionary)
There are fewer temperament variables than personality traits because these individual differences are
less salient and stable in children than in adults.
After the pioneering work of Thomas and Chess (1977), different taxonomies have been proposed in the
literature. A recent and very comprehensive is the one in Shiner R. And Caspi A. (2003) , which
individuates four main domains in child personality:
1)Extraversion/Positive Emotionality, with lower order traits of social inhibition, sociability,
dominance and activity level. 2) Neuroticism/Negative Emotionality with lower traits of anxious
distress (tapping fear and anxiety) and irritable distress (tapping anger and irritability).
3)Conscientiousness/Constraints, with lower traits of attention, inhibitory control and achievement
motivation. 4)Agreeableness
Little is still known about how these early emerging differences evolve into personality traits.
Measures:
9

There are different typologies of measures for children personality. It should be emphasized that all of
them have biases, and that therefore is always good to use more than one.
The typologies are, as indicated in Shiner and Caspi (2003):
1) Naturalistic observations (infants): Naturalistic observations are based on the direct observation of
the child's behavior in naturalistic settings, such as the child's home environment. In this procedure,
observers typically watch the child for periods of several hours and then use coding procedures for
observing a variety of specific behavioral tendencies.
The most used behavioural code is Eaton, Enns and Presse (1987), Journal of Psychoeducational
Assessment 3, 273-280. The code is available on the article. Some rate the child's temperament across a
variety of dimensions after the observation is completed, using one of the standard temperament
questionnaires
2) Questionnaires completed by parents or teachers (infants and children):
These questionnaires mainly study temperament along the lines of the “big five”
- IBQ (Infant Behavior Questionnaire): M. Rothbart
- CBQ (Children behaviour Questionnaire): Rothbart M, Ahadi S., Hershey K., Fisher P., (2001) “ Child
Development 72 (5), 1394-1408. The CBQ detected 15 primary temperament questionnaires, factor
analysis individuates 3 main factors: 1) extraversion/Surgency 2) Negative Affectivity 3) Effortful
Control. In appendix A of the paper there are scale definitions and sample items for CBQ.
http://www.bowdoin.edu/~sputnam/rothbart-temperament-questionnaires/
- ICID: Inventory Child Individual Differences: Halverson C., Havill V., Deal J. (2003):”Personality
Structure as derived from parental ratings of free descriptions of children: The Inventory of Child
Individual Differences” , Journal of Personality 71 (3). Representative items of ICID are in the
“Appendix A” of the article.
- (HiPIC): Mervielde I., De Fruyt F. (1999): “Construction of the hierarchical personality inventory for
Children” Hierarchical Personality Inventory for Children in Mervielde, Deary, DeFruyt,Ostendorf,
Personality Psychology in Europe:Proceedings of the Eight European Conference on Personality, 107-
127, Tillburg U Press.Couldn’t find the article on line.
Principal component analyses at the item level indicated that, for each age level, the first five principal
components tended to group items according to the Big Five. five domains: Conscientiousness,
Benevolence, Extraversion, Imagination, and Emotional Stability.
- Dutch BLIK: Slotboom, A., & Elphick, E. (1997). Parents’ perceptions of child personality:
Developmental precursors of the Big Five. Alblasserdam, The Netherlands:
Haveka b. v.
- Goldberg (2001):”Analyses of Digman’s child-personality data: Derivation of Big Five factor scores
from each of six samples. Journal of Personality, 69, 709–744.Working on Digman data on Hawaiian
children shows that the big five are derived as dimensions of personality of children.
3) Q-Sets (children) : an informant (parents, teachers, observers or clinicians) sorts a set of cards into a
quasi normal distribution based on how well each item describes the child. Also constructed along the
“big five” taxonomy.
The main two Q-Sets are:
- California Child Q-Set: Block J., Block JH.(1969/1980): “The California Child Q-Set” Palo Alto, CA:
Consulting Psychologists Press. Not publicly available. This Q-set consists of 100 cards; on each was a
descriptive statement which parents rank-ordered into nine categories ranging from "most descriptive"
to
"least descriptive" of their child.
- Common Language Q-set: Caspi A., Block J., Block JH., Klopp B., Lynam D., Moffit T., Stouthamer-
Loeber M. (1992): “A ‘common language’ version of the California Child Q set for personality
assessment” Psychological Assessment 4, 512-523. Represents the CCQset in a simpler language and
focuses especially on antisocial behavior. An example of the cards can be found at page. 520 of the
article.
4) Laboratory tasks (children):
- Laboratory procedures (LAB-TAB: Goldsmith, Rothbart, 1993): contains measures such as
Stranger Approach, Modified Peek-a-Boogame, and Puppet Game. In the lab, 20 episodes or games are
used to elicit reactions of frustration (anger), wariness (fear), interest, pleasure, and activity level.
10

- Preschool Lab-TAB (Goldsmith, Reilly, 1995):
Information on both at:
http://psych.wisc.edu/goldsmith/Researchers/GEO/lab_TAB.htm
The LAB-TAB manual can be downloaded at:
http://psych.wisc.edu/goldsmith/Researchers/GEO/Lab_TAB_download_info.htm
Some of the studies using LAB-TAB:
- Goldsmith, H. H., & Rieser-Danner, L. (1990). Assessing early temperament. In C. R. Reynolds & R.
Kamphaus (Eds.), Handbook of psychological and educational assessment of children. (vol. 2)
Personality, behavior, and context. (pp. 345-378). New York: Guilford Press.
- Goldsmith, H., Rieser-Danner, L., & Briggs, S. (1991). Evaluating convergent and discriminant validity
of temperament questionnaires for preschoolers, toddlers, and infants. Developmental Psychology, 27,
No. 4, 566-579.
5) Peer Nominations (adolescents): the peer group nominates who is the best described by a particular
item.
- Mervielde I., Defruyt F. (2000):”The Big Five Personality Factors as a model for the structure of
children’s peer nominations” European Journal of Personality 14, 91-106
- Masten A., Morison P., Pellegrini D., (1985): “A revised class method of peer assessment”,
Developmental Psychology 21, 523-533
6) Self Reports (adolescents, only recently also children).
- Vance H., Pumariega A. (2001):”Clinical Assessment of children and adolescents behaviour”, New
York: Wiley.
- Eder R.(1990): “Uncovering young children’s psychological selves: Individual and developmental
differences” child Development 61, 849-863. New method for understanding children individual
differences through self reports with a Puppet interview. The children are 4-8 years old, and the study
shows consistency of traits after one month.
Literature:
- Crespi A., Shiner R. (2003) “Personality Differences in childhood and adolescence: measurement,
development and consequences”, Journal of Psychology and Psychiatry 44(1), 2-32
- Shiner, R. (2006), “Temperament and personality in childhood”. In D. K. Mroczek & T. Little (Eds.),
Handbook of personality development (pp. 213-230). Mahwah, NJ, Erlbaum.
- Shiner, R. (Personality Differences in Childhood and adolescence: measurement, development and
consequences”, Journal of Child Psychology and Psychiatry, 44 (1), 2003, p.2-32
- Shiner R., Masten A., Roberts J.(2003). “Childhood personality foreshadows adult personality and life
outcomes two decades later”. Journal of Personality, 71, 1145-1170. Special issue: Personality
development.
- Thomas A., Chess S. (1977): “Temperament and Development” NY: Brunner/Mazel
Jerry Kagan:
- Kagan J., 1988, “Biological basis of childhood shyness”, Science, 240 (4849) , 167 - 171
Mary Rothbart:
- Rothbart, M. K. (1981). Measurement of temperament in infancy. Child Development, 52, 569-578.
- Rothbart, M, Bates, J (1998)”Temperament”, in Handbook of child psychology: Vol 3, Social,
emotional and personality development, Damon Eisemberg Eds,
a5. PSYCHOPATHOLOGY
A broad category comprising dysfunctional patterns of thought, feeling, or behavior. Most disorders are
included in the DSM-IV manual. Axis I disorders (e.g., depression) are more intense and
episodic/discreet, whereas Axis II disorders (i.e., personality disorders) are more tonic and enduring.
Measure:
For depression, the main scale is the Beck one. It indicates the acuteness of depression, but it is only for
adults.
- Beck, A., (1961) “An inventory for measuring depression “, Arch Gen Psychiatry, Archives of General
Psychiatry 4, 561-571
A version for kids is Achenbach Child Behavior Checklist:
11

- http://www.aseba.org/products/forms.html
Achenbach, T. M. (1991). Manual for the Child Behavior Check List/4–18 and 1991 Profile. Burlington:
University of Vermont.
- Another measure of Psychopathology is the Child Depression Index, which can be found in PSID.
Literature:
- Seligman, M.E.P., Walker, E., & Rosenhan, D.L. (2001). Abnormal psychology. (4th ed.) New York:
W.W. Norton. Manual in Abnormal Psychology.
- “Diagnostic and Statistical Manual of Mental Disorders”, (1995), American Psychiatric Association
Pub, Inc
b) Abilities
b1. IQ AND “G” FACTOR
“Ability to reason, solve problems, comprehend abstract associations, learn from experience and think
abstractly.”
One dominant factor “g” (“general factor”) refers to the first factor extracted from a factor analysis of
cognitive tasks, which many researchers consider to represent general (vs. specific) intelligence.”It
represents individuals’ abilities to perceive relationships and to derive conclusions from them. It is the
basic ability that underlies the performance of different intellectual tasks”
IQ specifically refers to one’s intelligence relative to one’s age group (especially for children)
Main authors: Arthur Jensen;; Nathan Brody
Measures:
WISC: Wechsler Intelligence Scale for Children
- Wechsler, D. (1974). Wechsler Intelligence Scale for Children-Revised (WISC-R). New York: The
Psychological Corporation.
WAIS
Raven’s Progressive Matrices
ASVAB
Literature:
- Neisser U., Boodoo G., Bouchard T., Boykin W., Brody N., Ceci S., Halpern D., Loehlin J., Perloff R.,
Sternberg R., Urbina S., (1996): “Intelligence: Knowns and Unknowns” American Psychologist 51(2),
77-101
- Gottfredson L., (1997) “Why “g” matters: the complexity of everyday life”, Intelligence.
- Brody, N., (1992). Intelligence. Boston: Academic Press
b2. SPECIFIC MENTAL ABILITIES
“Abilities as measured by tests of an individual in areas of spatial visualization, perceptual need,
number facility, verbal comprehension, word fluency, memory, inductive reasoning and so forth”
An umbrella category for lower-level mental abilities, including math, verbal, and spatial abilities, as
well as even more specific mental capacities. Measured by subtest scores on IQ tests
- Lubinski D. (2004), “Introduction to the special section on cognitive abilities: 100 years after
- Spearman's (1904) "‘General intelligence,’ objectively determined and measured." J Personality and
Social Psychology 86, 96–111
b3. CREATIVITY
Ability to generate novel ideas and behaviors that solve problems.
Measures:
One measure for creativity is Gough 1979 “Creative Personality Scale”:
- Gough, H. G. (1979). “A creative personality scale for the Adjective Check List”. Journal of Personality
and Social Psychology, 37,1398-1405. Link to the scale:
http://www.indiana.edu/~bobweb/Bob/Gough_Scale.doc
Literature:
- Eysenck, H.J. (1993): Creativity and Personality: Suggestions for a Theory, Psychological Inquiry,
- Eysenck, H.J. (1994), “The measurement of creativity. In Dimensions of Creativity, M.A. Boden.
- Cziksentmihalyi, M. (1996) “Creativity, Flow and the Psychology of Discovery and Invention”,
London: Harper Collins
12

- Simonton, D.(1984) , “Genius, Creativity and Leadership: Historiometric inquiries”, Harvard U Press.
- Simonton, D. (1997) “Genius and Creativity: selected papers” Ablex Ed.
b4. EXECUTIVE FUNCTION
“Higher level cognitive processes that organize and order behavior, including logic and reasoning,
abstract thinking, problem solving, planning and carrying out and terminating goal directed behavior”
Broad set of higher-level cognitive capacities attributed to the prefrontal cortex, often involving the
coordination and management of lower-level processes” (APA Dictionary)
Executive functions are unrelated to “g”, but determine more basic abilities that can vary considerably
among the population and that can be important in the process of acquiring cognitive skills: for
example, Kim, Whyte, Vaccaro et al. show how executive functions may relate to attention, while
Carpenter shows the relationship with working memory.
Executive functions are usually measured by neuropsychology tasks (e.g., go/no-go, Stroop, Continuous
Performance Task)
Literature:
- Miller E, Cohen J. (2001) "An integrative theory of prefrontal cortex function". Annual Review of
Neuroscience 24, 167-202
- Miller, E.K. (2000) The prefrontal cortex and cognitive control. Nature Reviews Neuroscience, 1:59-65
- Miller Lab, with links to publications: http://www.millerlab.org
- Blair C., Razza R.(2007), “Relating Effortful Control, Executive Function, and False Belief
Understanding to Emerging Math and Literacy Ability in Kindergarten” Child Development 78 (2),
647–663.
- Carpenter P., (2000): “Working memory and executive function”, Current Opinion in Neurobiology, 10,
195–19
b6. EMOTIONAL INTELLIGENCE
“Ability to process emotional information and use it in reasoning and other cognitive activities.
According to Mayer and Slovey 1997 model It comprises four abilities: to perceive and appraise
emotions accurately, to access and evoke emotions when they facilitate cognition, to comprehend
emotional language and make use of emotional information, and to regulate one’s own and others’
emotions to promote growth and well-being” Ability to perceive emotion, to integrate it in thought, to
understand it and to manage it (Mayer)” (APA Dictionary)
Emotion is here considered as a feeling state that conveys information about relationships (Mayer), and
intelligence refers to the ability of reason validly about this information.
Measures:
The most reliable measure is considered to be the MSCEIT test, but it is not publicly available.
The validity of self reported judgments is still highly debated
Literature:
- John Mayer website for emotional intelligence, with links to all relevant papers:
http://www.unh.edu/emotional_intelligence/.
See also:
- Mayer J., Slovey P., (1997) ”What is emotional intelligence? In P. Salovey y D. Sluyter Eds., Emotional
development and EI: Educational implications New York: Basic Books
- Mayer, J. Caruso, D., Salovey, P. (1999).”Emotional intelligence meets traditional standards for an
intelligence”. Intelligence, 27, 267-298.
- Mayer, J. D., Salovey, P., Caruso, D. R. (2000). Models of emotional intelligence. In R. J. Sternberg
(Ed.). Handbook of Intelligence (pp. 396-420). Cambridge, England: Cambridge University Press.
- Roberts R., Matthews G., Zeidner M., (2001), “Does Emotional Intelligence meet Traditional
Standards for Intelligence? Some New Data and Conclusions” Emotion, 1 (3), 196-231
http://www-users.york.ac.uk/~pjm21/psychometrics/robertsetal2001.pdf
- Roberts R., Matthews G., Zeidner M., (2004) “Emotional Intelligence: Science and Myth”, MIT Press,
Cambridge, Massachussets.
Goleman D.
13

Motivation and IQ Summary
Many of these studies used a within-subject design and measured the effect of motivation as the
differential between performance with and without incentives. An intriguing possibility for
measuring motivation not available at the time of these early studies is offered by Pailing &
Segalowitz (2004). A particular error-related ERP (event-related potential) is a metric for the
salience or importance of making an error. Subjects who are high in Conscientiousness or low in
Neuroticism showed dampened changes in this particular ERP when incentives were given for
improved performance.
Table 3. Studies documenting an effect of motivation at the time of test administration and IQ
score
Study
Edlund
(1972)
Ayllon &
Kelly (1972)
Sample 1
Ayllon &
Kelly (1972)
Sample 2
Ayllon &
Kelly
(1972)Sample
3
Sample and
Study Design
Between
subjects study.
11 matched
pairs of low
SES children;
children were
about 1 SD
below average
in IQ at
baseline
Within subjects
study. 12
mentally
retarded
children (avg
IQ 46.8)
Within subjects
study 34 urban
fourth graders
(avg IQ = 92.8)
Within subjects
study of 12
matched pairs
of mentally
retarded
children
Experimental
Group
M&M candies
given for each
right answer
Tokens given in
experimental
condition for
right answers
exchangeable for
prizes
Tokens given in
experimental
condition for
right answers
exchangeable for
prizes
Six weeks of
token
reinforcement for
good academic
performance
Effect size of
incentive (in
standard
deviations)
Experimental
group scored 12
points higher
than control
group during a
second testing
on an
alternative form
of the Stanford
Binet (about .8
SD)
6.25 points out
of a possible 51
points on
Metropolitan
Readiness Test.
t = 4.03
t = 5.9
Experimental
group scored
3.67 points out
of possible 51
points on a
post-test given
Summary
“…a carefully chosen
consequence, candy,
given contingent on
each occurrence of
correct responses to an
IQ test, can result in a
significantly higher IQ
score.”(p. 319)
“…test scores often
reflect poor academic
skills, but they may
also reflect lack of
motivation to do well
in the criterion
test…These results,
obtained from both a
population typically
limited in skills and
ability as well as from
a group of normal
children (Experiment
II), demonstrate that
the use of
reinforcement
procedures applied to a
behavior that is tacitly
1

Clingman &
Fowler
(1976)
Zigler &
Butterfield
(1968)
Within subjects
study of 72
first- and
second-graders
assigned
randomly to
contingent
reward,
noncontingent
reward, or no
reward
conditions.
Within and
between
subjects study
of 40 low SES
children who
did or did not
attend nursery
school were
tested at the
beginning and
end of the year
on Stanford-
Binet
Intelligence
Test under
either
optimized or
standard cdtns.
M&Ms given for
right answers in
contingent cdtn;
M&Ms given
regardless of
correctness in
noncontingent
cdtn
Motivation was
optimized
without giving
test-relevant
information.
Gentle
encouragement,
easier items after
items were
missed, etc.
under standard
conditions
higher than at
baseline;
control group
dropped 2.75
points. On a
second post-test
with incentives,
exp and control
groups
increased 6.25
and 7.17 points,
respectively
Only among
low-IQ (

Breuning &
Zella (1978)
Holt & Robbs
(1979)
Within and
between
subjects study
of 485 special
education high
school students
all took IQ
tests, then were
randomly
assigned to
control or
incentive
groups to retake
tests. Subjects
were belowaverage
in IQ.
Between and
within subjects
study of 80
delinquent boys
randomly
assigned to 3
experimental
groups and 1
control group.
Each exp group
received a
standard and
modified
administration
of the WISCverbal
section.
Incentives such
as record albums,
radios (

Larson (1994) Between
subjects study
of 109 San
Diego State
University
psychology
students
Duckworth
(in
preparation)
Within subjects
study of 61
urban lowachieving
high
school students
tested with a
groupadministered
Otis-Lennon IQ
test during their
freshman year,
then again 2
years later with
a one-on-one
(WASI) test
Up to $20 for
improvement
over baseline
performance on
cognitive speed
tests
Standard
directions for
encouraging
effort were
followed for the
WASI brief test.
Performance was
expected to be
higher because of
the one-on-one
environment.
“While both
groups
improved with
practice, the
incentive group
improved
slightly more.”
need to
calculate effect
size, but it was
not large
Performance on
the WASI as
juniors was
about 16 points
higher than on
the groupadministered
test as
freshmen.
Notably, on the
WASI, this
population
looks almost
“average” in
IQ, whereas by
Otis-Lennon
standards they
are low IQ. t
(60) = 10.67, p
< .001
2 reasons why
incentive did not
produce dramatic
increase: 1) few or no
unmotivated subjects
among college
volunteers, 2)
information processing
tasks are too simple for
‘trying harder’ to
matter
The increase in IQ
scores could be
attributed to any
combination of the
following 1) an
increase in “g” due to
schooling at an
intensive charter
school, 2) an increase
in knowledge or
crystallized
intelligence, 3) an
increase in motivation
due to the change in IQ
test format, and/or 4)
an increase in
motivation due to
experience at high
performing school
Ayllon, T., & Kelly, K. (1972). Effects of reinforcement on standardized test performance.
Journal of Applied Behavior Analysis. Vol., 5(4), 477-484.
Breuning, S. E., & Zella, W. F. (1978). Effects of individualized incentives on norm-referenced
IQ test performance of high school students in special education classes. Journal of
School Psychology, 16(3), 220-226.
Clingman, J., & Fowler, R. L. (1976). The effects of primary reward on the I.Q. performance of
grade-school children as a function of initial I.Q. level. Journal of Applied Behavior
Analysis, 9(1), 19-23.
Edlund, C. V. (1972). The effect on the behavior of children, as reflected in the IQ scores, when
reinforced after each correct response. Journal of Applied Behavior Analysis. Vol., 5(3),
317-319.
4

Holt, M. M., & Hobbs, T. R. (1979). The effects of token reinforcement, feedback and response
cost on standardized test performance. Behaviour Research and Therapy, 17(1), 81-83.
Larson, G. E., Saccuzzo, D. P., & Brown, J. (1994). Motivation: Cause or confound in
information processing/intelligence correlations? Acta Psychologica, 85(1), 25-37.
Pailing, P. E., & Segalowitz, S. J. (2004). The error-related negativity as a state and trait
measure: Motivation, personality, and ERPs in response to errors. Psychophysiology,
41(1), 84-95.
Zigler, E., & Butterfield, E. C. (1968). Motivational Aspects of Changes in Iq Test Performance
of Culturally Deprived Nursery School Children. Child Development, 39(1), 1-14.
5

Ability Bias, Errors in
Variables and Sibling Methods
James J. Heckman
University of Chicago
Econ 312
This draft, May 26, 2006
1

1 Ability Bias
Consider the model:
log = 0 + 1 +
where =income, = schooling, and 0 and 1 are parameters
of interest. What we have omitted from the above
specification is unobserved ability, which is captured in the
residual term .Wethusre-writetheaboveas:
log = 0 + 1 + +
where is ability, ( 0 ) ( 0), andwebelievethat
( ) 6= 0.Thus,( | ) 6= 0,sothatOLS on our
original specification gives biased and inconsistent estimates.
2

1.1 Strategies for Estimation
1. Use proxies for ability: Find proxies for ability and include
them as regressors. Examples may include: height,
weight, etc. The problem with this approach is that proxies
may measure ability with error and thus introduce
additional bias (see Section 1.3).
3

2. Fixed Eect Method: Find a paired comparison. Examples
may include a genetic twin or sibling with similar or
identical ability. Consider two individuals and 0 :
log log 0 = ( 0 + 1 + ) ( 0 + 1 0 + 0 )
= 1 ( 0)+( 0)+( 0 )
Note: if = 0,thenOLS performedonourfixedeect
4

estimator is unbiased and consistent. If 6= 0,thenwe
just get a dierent bias (see Section 1.2). Further, if is
measured with error, we may exacerbate the bias in our
fixed eect estimator (see Section 1.3).
1.2 OLS vs. Fixed Eect (FE)
In the OLS case with ability bias, we have:
plim (
1 )= 1 +
( )
()
(See derivation of Equation (2.2) for more background on the
above derivation).
5

We also impose:
() = ( 0 )
( ) = ( 0 0 )
( 0 ) = ( 0 )
With these assumptions, our fixed eect estimator is given by:
plim
1 = 1 + ( 0 ( 0 )+( 0 ))
( 0 )
= 1 + ( ) (0 )
() ( 0 ) .
Note that if ( 0 )=0 and ability is positively correlated
with schooling, then the fixed eect estimator is upward biased.
6

From the preceding, we see that the fixed eect estimator has
more asymptotic bias if:
( ) ( 0 )
() ( 0 )
( )
()
( )
()
(0 )
( 0 ) .
7

1.3 Measurement Error
Say = + where is observed schooling. Our model
now becomes:
log = 0 + 1 + = 0 + 1 +( + 1 )
and the fixed eect estimator gives:
log log 0 = ( 0 + 1 + ) ( 0 + 1 0 + 0 )
= 1 ( 0 )+( 0 )+ 1 ( 0 )
Now we wish to examine which estimator (OLS or fixed eect),
has more asymptotic bias given our measurement error problem.
For the remaining arguments of this section, we assume:
( | ) = ( 0 | ) = ( | 0 )=0
so that the OLS estimator gives:
8

plim
1 = 1 + ( + 1 )
( )
= 1 + ( ) 1()
.
()+()
The fixed eect estimator gives:
plim
1 = 1 +
³
´
0 ( 0 )+ 1 ( 0 )
( 0 )
= 1 + (( 0 ) ( 0 )) 1 ( 0 )
( 0 )+( 0 )
= 1 + ( ) ( 0 ) 1 ()
()+() ( 0 )
.
9

Under what conditions will the fixed eectbiasbegreater?
From the above, we know that this will be true if and only if:
( ) ( 0 ) 1 ()
( ) 1()
()+() ( 0 ) ()+()
( 0 )(()+())
( 1 () ( )) ( 0 )
( ) 1()
()+()
( 0 )
( 0 ) .
If this inequality holds, taking dierences can actually worsen
the fit over OLS alone. Intuitively, we see that we have dierenced
out the true component, , and compounded our measurement
error problem with the fixed eect estimator.
10

In the special case = 0 , the condition is
1 ()
()+() ( 0 ) ( ) 1()
()+()
11

2 Errors in Variables
2.1 The Model
Suppose that the equation for earnings is given by:
= 1 1 + 2 2 +
where ( | 1 2 )=0 0 .Alsodefine:
1 = 1 + 1 and 2 = 2 + 2
12

Here, 1 and 2 are observed and measure 1 and 2 with
error. We also impose that . So, our initial model
can be equivalently re-written as:
= 1 1 + 2 2 +( 1 1 2 2 ).
Finally, by assumed independence of and , wewrite:
= + .
13

2.2 McCallum’s Problem
Question: Is it better for estimation of 1 to include other variables
measured with error? Suppose that 1 is not measured
with error, in the sense that 1 =0 while 2 is measured with
error. In 2.2.1 and 2.2.2 below, we consider both excluding
and including 2 and investigate the asymptotic properties
of both cases.
2.2.1 Excluded 2
The equation for earnings with omitted 2 is:
= 1 1 +( + 2 2 )
14

Therefore, by arguments similar to those in the appendix, we
know:
plim ˜ 1 = 1 + 12
2 . (2.1)
11
Here, 12 is the covariance between the regressors, and 11 is
thevarianceof 1 Before moving on to a more general model
for the inclusion of 2 let us first consider the classical case
for including both variables. Suppose
¸ ¸
= 11 0
11 0
0 =
.
22
0 22
We know that:
plim ˆ = £ ( ) 1 ( ) ¤ (2.2)
15

where the coecient and regressor vectors have been stacked
appropriately (see Appendix for derivation). Note that represents
the variance-covariance matrix of the measurement errors,
and is the variance-covariance matrix of the regressors.
Straightforward computations thus give:
plim ˆ
=
=
"
11 +
11 0
0 22 + 22
11
0
11 + 11
22
0
22 + 22
¸1
11 0
1
¸
2
0 22
¸#
1
2
¸
16

2.2.2 Included 2
In McCallum’s problem we suppose that 12 =0 Further, as
1 is not measured with error, 11 =0 Substituting this into
equation 2.2 yields:
¸1 ¸
plim ˆ
11
=
12 0 0
12 22 + 22 0
22
With a little algebra, the above gives:
μ
plim ˆ
12
1 = 1 + 2
22
11
22 + 22 2
12
μ μ
11
12
22
= 1 + 2
11 22 (1 2 12)+ 22
17

where 2 12 is simply the correlation coecient,
we know that:
0 2 12 1
2 12
11 22
Further,
so including 2 results in less asymptotic bias (inconsistency).
(We get this result by comparing the above with the bias from
excluding 2 in section 2.2.1, the result captured in equation
(2.1)). So, we have justified the kitchen sink approach. This
result generalizes to the multiple regressor case - 1 badly measured
variable with good ones (Econometrica, 1972).
18

2.3 General Case
In the most general case, we have:
plim ˆ = ( ) 1
11 +
= 11 12 + ¸1
12
11
12 + 12 22 + 12
22 12 22
With a little algebra we find:
¸
1
2
¸
.
det( )= 11 22 + 11 22+ 11 22 + 11 22 2
122 12 12 2 12
19

Therefore:
plim ˆ =
1
det( )
×
11 12
12 22
Supposing 12 =0 we get:
22 + 22 ( 12 + 12)
( 12 + 12) 11 + 11
¸ ¸
1
2
¸
det(˜ )=det( ) |
12
=0
= 11 22 + 11 22 + 11 22 + 11 22 2 12
20

and thus:
plim ˆ =
( 22 + 22) 11
det(˜ )
11 12
det(˜ )
12 22
det(˜ )
( 11 + 11) 22
det(˜ )
¸
1
2
Note that if 2 12 0 OLS may not be downward biased for
1 .If 2 =0 we get:
plim ˆ 2 = 1 12 11
det(˜ )
so, if 2 were a race variable and blacks get lower quality
schooling, (where schooling is measured by 1 ) then 12 0
and hence ˆ 2 0 This would be a finding in support of labor
market discrimination.
21

2.4 The Kitchen Sink Revisited
McCallum’s analysis suggests that one should toss in a variable
measured with error if there is no measurement error in 1
But suppose that there is measurement error in 1 Is it still
better to include the additional variable measured with error
as a regressor? We proceed by imposing 2 =0.
(i) Excluded X 2 . The equation for earnings with measurement
error in 1 and excluded 2 is:
= (1 + 1 ) 1 +( + 2 2 )
= 1 1 +( + 2 2 + 1 1 )
22

Therefore:
μ
plim ˜
1 = 1 11
1
11 + 11
= 1
1
1+ 11
μ
11
= 1
11 + 11
11
(2.3)
(ii) Included X 2 . From our analysis in the General Case
(Section 2.3), we know that:
μ
plim ˆ
(22 +
1 =
22) 11 2 12
1 . (2.4)
det(˜ )
23

If 22 =0 so that 2 is not measured with error:
μ
plim ˆ 11 22 2 12
1 = 1
11 22 2 12 +
Ã
11 22
1 2 12
= 1 .
1 2 12 + 11
11
!
(2.5)
Comparing eqn (2.4) and eqn (2.5), we see that adding the
variable measured without error always exacerbates the bias.
24

For, the bias in the excluded case will be smaller if:
1
1
1+
1 2 12
1
11
1 2 12 +
11
11
μ
μ 11
1 2 12 + 11
1+ 11 ¡1 ¢
2
11 12
11
0 2 12
11
11
whichisalwaysthecase,provided 2 12 0 (Notethatthe
coecients on 1 for both the excluded and included case are
less than one. So, the larger coecient is the one with less
bias, as stated above.)
25

Now suppose that 22 0 so that both variables are measured
with error. Then:
μ
plim ˆ
(22 +
1 =
22) 11 2 12
1
det(˜ )
1+ 22
2 12
= 1
22
1+ 11
+ 11 22
+
22
2 12
11 11 22 22
Intuitively, adding measurement error in 2 can only worsen
the bias, and thus exclusion should again be preferred to inclusion.
Formally, including 2 givesmorebiasifandonly
26

if:
1
1+ 11
11
+ 11
1+ 22
22
2 12
1
22
+ 22
2 12
11 22
μ μ 22
1+ 11
1+ 22
2 12
11 22
μ
1+ 11
11
+ 11
11
22
22
+ 22
22
2 12
1
11
1+ 11
2 12
11
11
0.
27

Thus, provided 2 12 0 including 2 resultsinmorebias
than excluding it. If 2 12 =0 the bias from including 2 is
obviously seen to be:
1
1+ 22
22
1+ 11
11
+ 11
11
22
22
+ 22
22
= 1
1+ 22
μ
22
22
= 1
1
1+
11
11
μ
1+ 22
1+ 11
so that including and excluding 2 yields the same result.
11
28

Finally, from the General Case section, we have:
plim ˆ 1 = 1 ( 22 + 22) 11 2 12 + 2 ( 12 22)
.
11 22 2 12 + 11 22 + 11 22 + 11 22
L’Hôpital’s rule on the above shows that:
11 lim
³plim 1´
ˆ
³
lim plim
1´
ˆ = 1 11 + 2 12
22 11 + 11
=0 and
=
1 11
11 + 11
+ 2 12
.
11 + 11
29

Appendix
Derivation of Equation (2.2)
We can write
= +( 1 1 2 2 )
where:
= £ 1 2
¤
and =
1
2
¸
and 1 2 are × 1.
30

So:
³
ˆ = 0 ´1 (
0 )
= +
³ 0 ´1 ³
´
0 ( 1 1 2 2 )
á
0 ¢ ! 1
= +
μμ μ μ
0
0 1
×
1
0 2
2
³
+
³ 0 ´´1
³ ³ ´
× 0
³ 0 1´
1
³ 0 2´
2´
31

¡
0
=
¡ 0
μ
×
=
¡
0
×
1 1
2 1
0
1 1
0
1 1
¢ ¡
0
¢ ¡
0
1 2
2 2
¢
¢
¸
2 1 +
1
¢ ¡ ¢
0
¢ ¡ 1 2¢
0
¡ 0
2 1 2 2
¡ ¢ ¡
0
1 1
0
¡ 0
2 1
¢
¡
0
¢
1 2
¢
2 2
¸1
¸
0
1 2
0
2 2
¸1
¸
1
2
¸
2
32

=
¡¡
0
¢ ¢ ¡¡ μ ( ) 1 1 + 0 0
¢ ¢
1 1 1 + 0 1 2
¡¡ ¢ ¢ ¡¡
0 2 + 0 0
¢ ¢
2 1 2 + 0 2 2
¸
1
×
2
= ¡ ( ) 1 ( ) ¢
¸
where the second-to-last step follows from the independence
of and This type of argument is also used to derive the
probability limit of the ’s in section 1.
33

3 Sibling Models: Components of Variance
Scheme
Suppose that data on two brothers, say and is at our
disposal Without loss of generality, we will consider how to
estimate parameters of interest for person in what follows.
We will begin by introducing a general model and then focus on
the two-person case mentioned above. Consider the following
triangular system:
1 = 1
2 = 12 1 + 2
3 = 13 1 + 23 2 + 3
34

Here, indexes the person in the group. We assume
that and 0
are uncorrelated (i.e., uncorrelated across
groups). Further, we suppose:
= +
= + ,
for =1 2 3. We assume is uncorrelated across equations
and across within the group, is i.i.d. across groups, and
is i.i.d. within groups and uncorrelated with .
35

3.1 Estimation
We specialize the above model into a two person framework and
propose a similar three equation system. Let 1 = early (preschool)
test score, 2 = schooling (years), and 3 = earnings.
It seems plausible to write the equation system
1 = + 1
2 = 2 + 2
3 = 23 2 + 3 + 3
where = ability. Regressing 3 on 2 clearly gives biased
estimates of 23 as ( | 2 ) 6= 0 If 3 0 then OLS estimates
of 23 are upward biased. One estimation approach is to use
1 as a proxy for ability:
3 = 23 2 + 3 ( 1 1 )+ 3
36

However, this results in a similar problem – regressing 3 on
1 and 2 will give biased estimates as 1 is correlated with our
residual. (i.e., 1 is an imperfect proxy).
Solutions:
Onesolutionistouse 1 as an instrument for 1 Why
is this a valid IV ? From our construction of the model, we
know that the are uncorrelated across equations and groups.
Further, test scores are correlated across siblings. That is,
( 1 1 ) 6= 0by our group structure.
Another solution is possible if there exists an additional early
reading on the same person:
0 = 0 + 0
Then if 0 6=0 0 is a valid proxy for 1 and we can perform
2SLS.
37

3.2 Griliches and Chamberlain model
Here we have a modified triangular system as follows:
1 = 1 + 1
2 = 12 1 + 2 + 2
3 = 13 1 + 23 2 + 3 + 3
where 1 = years schooling, 2 = late test score (SAT), and
3 = earnings. Notethattherearealternativemodelswith
other dependent variables. For example, { 1 = schooling, 2 =
early earnings, and 3 = late earnings}, and { 1 = schooling,
2 = consumption, and 3 = earnings}. Getting the equation
system into reduced form and expressing as matrix notation,
we write
= + ,
38

where:
and:
=
=
1
2
=
3
1
2
=
3
1
2 + 12 1
3 + 13 1 + 23 ( 2 + 12 1 )
1
2 + 12 1
3 + 13 1 + 23 ( 2 + 12 1 )
Estimation. For estimation, we impose that 23 =0 In our
second example of section 3.2, this would be equivalent to stating
that there is no correlation between transient income and
consumption (permanent income hypothesis). In general, with
one factor, we need one more exclusion than that implied by
triangularity.
39

(i) 1 proxies .
so that
= 1 1
1
2 = 2
1
1 2
1
1 + 2 .
We can then estimate 2
1
consistently by using 1
as an instrument for 1 in the equation above.
(ii) Get residuals from (i): = 2 2
1
1 .
(iii). Use the residuals as an instrument for 1 in
the 3 equation. is valid since it is both uncorrelated
with and 3 and it is correlated with 1 :
40

μ
( 1 )= 1 2
2
1
μ
1
= 1 + 1 2 + 12 1
2 + 12 1
1
μ
1
= 1 + 1 2
2
1 6= 0
1
if 1 6=0 and, 2 6=0 Thus we can estimate 13 .
(iv). Interchange the role of 2 and 3 to estimate
12 .
(v). Form the residual (and recall that 13 is known
and 23 =0)
= 3 13 1 = 3 + 3 .
41

(vi) Use 1 as a proxy for ability. Substituting this
into gives:
= 3
1
1 + 3
1
3 3
1
1 .
(vii) Now use 1 as an instrument for 1 in the
above to get an estimate of 3
1
.
(viii) Interchange the role of 2 and 3 to estimate 2
1
.
42

3.3 Triangular systems more generally
Without loss of generality, suppose that 2 is excluded from the
equation of our system. (We are supposing the existence
of an extra exclusion than that implied by triangularity). We
seek to estimate the parameters of the system in equation as
well as equations before and after
Equation t.
i. Use 1 as a proxy for ability. Solving for and
substituting into the equation:
= +
43

We get:
=
1
1
1
1 +
and we are considering =2 1 The ratio
1
can then
be identified using 1 as an instrument for 1
ii. Form the residuals:
=
1
1 =2 1
Now we have 2 IV’s ( 2 3 1 ) for the
2 independent variables in the equation (
1 3 1 ) so we can consistently estimate the
coecients in the equation.
44

Equations before t.
iii. Form:
= 1 1 ··· 1 1
We can use 1 ··· 1
to form 1 purged
IV ’s and is used as a proxy for unobserved ability,
. Inthisway,wecanestimatealloftheparameters
in equations (Note the sequential
order implicit in this triangular system. We must
first estimate before this step can be made.)
Example. Suppose 3 and
3 = 13 1 + 23 2 + 3 + 3 .
45

Use = + as a proxy for . Substituting this into our
3 equation yields:
3 = 13 1 + 23 2 + μ
3
+ 3
3
.
Observe that 1 and 2 , are independent of our residual, but
is not. We can use as an instrument for to estimate the
parameters above. This obviously generalizes for all equations
less than .
46

Equations after t.
iv. Assume identification for all equations through
via an exclusion restriction in equation .
Example. As an example, consider the following:
Define:
4 = 14 1 + 24 2 + 34 3 + 4 + 4
2 2 12 1 3 3 13 1 23 2
Solving for 1 and 2 and substituting into the equation for 4
we find:
47

4 = 14 1 + 24 2 + 34 ( 3 + 13 1 + 23 2 )+ 4 + 4
where:
= ( 14 + 34 13 ) 1 +( 24 + 34 23 ) 2 + 34 3 + 4 + 4
= ( 14 + 34 13 ) 1 +( 24 + 34 23 )(2 + 12 1 )
+ 34 3 + 4 + 4
= 14 1 + 24 2 + 34 3 + 4 + 4
14 = 14 + 24 12 + 34 13
24 = 24 + 23 34
Using 1 as a proxy for and substituting we get:
μ
4 = 1 1 + 242 + 34 3 + 4
4
1
1
48

where 1 = 14 + 4
1
We can then use 1 2 and 3 as
instruments to get an estimate of 34 Define:
˜ 4 = 4 34 3 = 14 1 + 24 2 + 4 + 4
(Excluding 3 allows us to estimate the remaining parameters).
Using 3 as a proxy for yields:
4 = 14 1 + 24 2 + μ
4
3 + 4
4
3 .
3 3
We can then estimate 14 and 24 by using 1 2 and 3 as
an IV. We can continue estimating. For example, consider the
5 equation:
(i) Rewrite in terms of 1 2 3 and 4
49

(ii) Use 1 to proxy .
(iii) Use a cross-member IV for 1 in addition to
=2 3 4 which gives our estimate of 45
(iv) Now form ˜ 5 = 5 45 4
(v) With 4 excluded, we can use purged IV ’s on
˜ 5 as before.
50

3.4 Comments
1. One needs to check the rank order conditions
for identification (requires imposing an exclusion
restriction).
2. Griliches and Chamberlain (IER, 1976) find a
small ability bias - 3 decimal point dierence in
schooling coecient.
51

4 Twin Methods
Basic Principle: Monozygotic or MZ (identical) twins are
more similar than Dizygotic or DZ (fraternal) twins. The key
assumption is that if environmental factors are the same for
both types of twins, then we can estimate genetic components
to outcomes.
52

4.1 Univariate Twin Model
Let = observed phenotypic variable, = unobserved genotype,
and = environment. Further, suppose that we can
write our model additively:
= +
and assume independence of and so that 2 = 2 + 2 .
Now suppose that we have data on another individual:
= +
Then our phenotypic covariance is:
( )= ( )+ ( )
53

where we are imposing the assumption:
( )=( )=0
Defining standardized forms and some simplifying notation, let
˜
˜
˜
2 2
2
2 2
2
Thus, ˜ =˜ +˜ which implies ˜ = ˜ + ˜ We can
also derive the identity:
2 + 2 = 2
2
+ 2
2
=1
where the last step follows from our assumption of independence.
Now we wish to consider the correlation between ob-
54

served phenotypes of our two individuals:
= ( )
= (˜ + ˜ ˜ + ˜ )
= 2 (˜ ˜ )
+ 2 (˜ ˜ )
(˜) (˜)
= 2 + 2
say, with and defined as above. We assume that =1
and that 1 Thatis,thegenotypicvariableisperfectly
correlated among identical twins, but less than perfectly correlated
among fraternal twins. Replacing this result into the
above produces:
= 2 + 2
= 2 + 2
55

Therefore:
= (1 ) 2 +( ) 2
= (1 ) 2 +( )(1 2 )
where the last equality follows from our established identity.
Solving for 2 , we find:
2 = ( ) ( )
(1 ) ( )
The only known in the right hand side of the above equality is
the expression ( ), which is simply the correlation
coecient of the observed phenotypic variable. The remaining
two expressions, (1 ) and ( ) can not be computed
as they represent statistics on variables we don’t observe.
56

One could impose = so that:
2 =
1
The expression is a measure of how closely the genetic
variable is correlated across our two observations. One could
then guess or estimate a value for this parameter to derive
corresponding estimates of 2 the ratio of how much variance
in the phenotypic variable is explained by variance in the genetic
component. Other studies have attempted to include
( ) 6= 0but this presents an identification problem. A
typical value of the estimable portion of the above, ,
is commonly reported in the literature to be 02.
57

Web Appendix D
Notes on Figure 2, Predictive Validities of IQ and Big Five Dimensions
Leadership
Associations between personality and IQ and leadership were taken from two meta-analyses
conducted by the same research group (Judge, Bono, Ilies, and Gerhardt, 2002; Judge, Colbert,
and Ilies, 2004). Leadership was defined jointly as “leader emergence,” the degree to which the
individual is viewed as a leader by others, and “leader effectiveness,” performance in influencing
and guiding the activities of a group. Typically, these assessments were made by subordinates,
supervisors, peers, or observers. Studies relying on self-report assessments of leadership were
not included.
Estimated true score correlations between IQ and leadership were corrected for unreliability in
the predictor and criterion, as well as for range restriction. Estimated true score correlations
between personality and leadership were corrected for reliability in the predictor and criterion,
but not for range restriction.
Job Performance
Associations between personality and job performance were taken from a meta-analysis (Barrick
and Mount, 1991). Job performance was defined by three criteria: job proficiency (primarily
assessed by performance ratings), training proficiency (primarily assessed by training
performance ratings), and personnel data (including salary level, turnover, status change, and
tenure).
The association between IQ and job performance was taken from (Hogan, 2005). This article did
not state whether this correlation is observed or corrected. The much higher estimate of corrected
validity is offered by Schmidt and Hunter (2004). Concerns about over-correction with respect to
restriction on range and reliability have been raised by Hartigan and Wigdor (1984) with specific
reference to estimating the effect of IQ on job performance.
Longevity
Associations between personality and longevity were taken from a review of 34 studies that were
prospective in design and which controlled for demographic factors (Roberts, Kuncel, Shiner,
Caspi, and Goldberg, in press). Estimated true score correlations were not provided.
Years of Education
Cross-sectional associations between personality and years of schooling were taken from a study
(Goldberg, Sweeney, Merenda, and Hughes, 1998) using a large (N = 3629) sample of
individuals representative of working adults in the U.S. in the year 2000. Estimated true score
correlations were not provided.
1

The association between IQ and years of schooling was taken from a review by an American
Psychological Association (APA) taskforce (Neisser et al., 1996). This article did not state
whether this correlation was observed or corrected.
College Grades
Associations between personality and college academic performance were taken from a metaanalysis
of 23 studies (collective N = 5878) by O’Connor and Paunonen (in press). Most of the
reviewed studies used as measures of academic performance GPA, but several also used course
exam grades.
The association between IQ and college GPA is from a review (Jensen, 1998). This article did
not state whether this correlation is observed or corrected. A similar estimate (r = .5) is offered
by Neisser et al. (1996) for the association between IQ and general academic performance.
References
Barrick, M.R. and Mount, M.K. (1991). The big five personality dimensions and job
performance: A meta-analysis. Personnel Psychology, 44, 1-26.
Goldberg, L. R., Sweeney, D., Merenda, P. F., and Hughes, J. E., Jr. (1998). Demographic
variables and personality: The effects of gender, age, education, and ethnic/racial status
on self-descriptions of personality attributes. Personality and Individual Differences,
24(3), 393-403.
Hartigan and Wigdor (1984).
Hogan, R. (2005). In Defense of Personality Measurement: New Wine for Old Whiners. Human
Performance, 18(4), 331-341.
Jensen, A. R. (1998). The g Factor: The Science of Mental Ability. Westport, CT: Praeger.
Judge, T. A., Bono, J. E., Ilies, R., and Gerhardt, M. W. (2002). Personality and leadership: A
qualitative and quantitative review. Journal of Applied Psychology, 87(4), 765-780.
Judge, T. A., Colbert, A. E., and Ilies, R. (2004). Intelligence and Leadership: A Quantitative
Review and Test of Theoretical Propositions. Journal of Applied Psychology, 89(3), 542-
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